Rapid flotation of mediated by high light exposure: implications for surface scum formation and cyanobacterial species succession.

Increasing occurrences of surface scum have been observed in the context of global climate change and the increase in anthropogenic pollution, causing deteriorating water quality in aquatic ecosystems. Previous studies on scum formation mainly focus on the buoyancy-driven floating process of larger colonies, neglecting other potential mechanisms. To study the non-buoyancy-driven rapid flotation of , we here investigate the floating processes of two strains of single-cell species ( and ), which are typically buoyant, under light conditions (150 μmol photons s m).

Dynamics of surface scum formation under different wind conditions: the role of hydrodynamic processes at the air-water interface.

Due to climate change, blooms occur at increasing frequencies in aquatic ecosystems worldwide. Wind-generated turbulence is a crucial environmental stressor that can vertically disperse the surface scum, reducing its light availability. Yet, the interactions of scum with the wind-generated hydrodynamic processes, particularly those at the air-water interface, remain poorly understood.

Contrastive representation learning on dynamic networks.

Representation learning for dynamic networks is designed to learn the low-dimensional embeddings of nodes that can well preserve the snapshot structure, properties and temporal evolution of dynamic networks. However, current dynamic network representation learning methods tend to focus on estimating or generating observed snapshot structures, paying excessive attention to network details, and disregarding distinctions between snapshots with larger time intervals, resulting in less robustness for sparse or noisy networks. To alleviate these challenges, this paper proposes a contrastive mechanism for temporal representation learning on dynamic networks, inspired by the success of contrastive learning in visual and static network representation learning.

Selection of photosynthetic traits by turbulent mixing governs formation of cyanobacterial blooms in shallow eutrophic lakes.

Prediction of the complex cyanobacteria-environment interactions is vital for understanding harmful bloom formation. Most previous studies on these interactions considered specific properties of cyanobacterial cells as representative for the entire population (e.g.

A Novel Sequence-to-Sequence-Based Deep Learning Model for Multistep Load Forecasting.

Load forecasting is critical to the task of energy management in power systems, for example, balancing supply and demand and minimizing energy transaction costs. There are many approaches used for load forecasting such as the support vector regression (SVR), the autoregressive integrated moving average (ARIMA), and neural networks, but most of these methods focus on single-step load forecasting, whereas multistep load forecasting can provide better insights for optimizing the energy resource allocation and assisting the decision-making process. In this work, a novel sequence-to-sequence (Seq2Seq)-based deep learning model based on a time series decomposition strategy for multistep load forecasting is proposed.

On secondary structure avoidance of codes for DNA storage.

A secondary structure in single-stranded DNA refers to its propensity to undergo self-folding, leading to functional inactivity and irreparable failures within DNA storage systems. Consequently, the property of secondary structure avoidance (SSA) becomes a crucial criterion in the design of single-stranded DNA sequences for DNA storage, as it prohibits the inclusion of reverse-complement subsequences that contribute to such structures. This work is specifically focused on addressing the avoidance of secondary structures in single-stranded DNA sequences.

DRL-Based URLLC-Constraint and Energy-Efficient Task Offloading for Internet of Health Things.

Internet of Health Things (IoHT) is a promising e-Health paradigm that involves offloading numerous computational-intensive and delay-sensitive tasks from locally limited IoHT points to edge servers (ESs) with abundant computational resources in close proximity. However, existing computation offloading techniques struggle to meet the burgeoning health demands in ultra-reliable and low-latency communication (URLLC), one of the 5G application scenarios. This article proposes a Multi-Agent Soft-Actor-Critic-discrete based URLLC-constrained task offloading and resource allocation (MASACDUA) scheme to maximize throughput while minimizing power consumption on the remote side, considering the long-term URLLC constraints.

Effect of light-mediated variations of colony morphology on the buoyancy regulation of Microcystis colonies.

Light is an important driver of algal growth and for the formation of surface blooms. Long-term buoyancy maintenance of Microcystis colonies is crucial for their aggregation at the water surface and the following algal bloom development. However, the effect of light-mediated variations of colony morphology on the buoyancy regulation of Microcystis colonies remains unclear.

Multiple errors correction for position-limited DNA sequences with GC balance and no homopolymer for DNA-based data storage.

Deoxyribonucleic acid (DNA) is an attractive medium for long-term digital data storage due to its extremely high storage density, low maintenance cost and longevity. However, during the process of synthesis, amplification and sequencing of DNA sequences with homopolymers of large run-length, three different types of errors, namely, insertion, deletion and substitution errors frequently occur. Meanwhile, DNA sequences with large imbalances between GC and AT content exhibit high dropout rates and are prone to errors.

A Secure High-Order Gene Interaction Detection Algorithm Based on Deep Neural Network.

Identifying high-order Single Nucleotide Polymorphism (SNP) interactions of additive genetic model is crucial for detecting complex disease gene-type and predicting pathogenic genes of various disorders. We present a novel framework for high-order gene interactions detection, not directly identifying individual site, but based on Deep Learning (DL) method with Differential Privacy (DP), termed as Deep-DPGI. Firstly, integrate loss functions including cross-entropy and focal loss function to train the model parameters that minimize the value of loss.

Expectation and Complaint: Online Consumer Complaint Behavior in COVID-19 Isolation.

Purpose: This study aims to articulate the nature of consumer complaining behavior (CCB) by analyzing the mechanism and characteristics of online CCB in COVID-19 isolated environment.

Patients And Methods: For the purpose, this study collected data via a web-based questionnaire survey from 408 consumers in Shanghai, China during COVID-19 isolation. Through building and analyzing a structural equation model that consists of six latent variables such as perceived service quality, perceived product quality, customer satisfaction, negative emotion, customer complaint; the study analyzed the basic characteristics of CCB, and focused on the moderation test of consumer expectation to validate its important role in consumer decision-making behavior.

Clover: tree structure-based efficient DNA clustering for DNA-based data storage.

Deoxyribonucleic acid (DNA)-based data storage is a promising new storage technology which has the advantage of high storage capacity and long storage time compared with traditional storage media. However, the synthesis and sequencing process of DNA can randomly generate many types of errors, which makes it more difficult to cluster DNA sequences to recover DNA information. Currently, the available DNA clustering algorithms are targeted at DNA sequences in the biological domain, which not only cannot adapt to the characteristics of sequences in DNA storage, but also tend to be unacceptably time-consuming for billions of DNA sequences in DNA storage.

Exploring Temporal Community Structure via Network Embedding.

Temporal community detection is helpful to discover and analyze significant groups or clusters hidden in dynamic networks in the real world. A variety of methods, such as modularity optimization, spectral method, and statistical network model, has been developed from diversified perspectives. Recently, network embedding-based technologies have made significant progress, and one can exploit deep learning superiority to network tasks.

HB-DSBM: Modeling the Dynamic Complex Networks From Community Level to Node Level.

A variety of methods have been proposed for modeling and mining dynamic complex networks, in which the topological structure varies with time. As the most popular and successful network model, the stochastic block model (SBM) has been extended and applied to community detection, link prediction, anomaly detection, and evolution analysis of dynamic networks. However, all current models based on the SBM for modeling dynamic networks are designed at the community level, assuming that nodes in each community have the same dynamic behavior, which usually results in poor performance on temporal community detection and loses the modeling of node abnormal behavior.

Role Discovery-Guided Network Embedding Based on Autoencoder and Attention Mechanism.

Recently, network embedding (NE) is an amazing research point in complex networks and devoted to a variety of tasks. Nearly, all the methods and models of NE are based on the local, high-order, or global similarity of the networks, and few studies have focused on the role discovery or structural similarity, which is of great significance in spreading dynamics and network theory. Meanwhile, existing NE models for role discovery suffer from two limitations, that is: 1) they fail to model the varying dependencies between each node and its neighbor nodes and 2) they cannot capture the effective node features which are helpful to role discovery, which makes these methods ineffective when applied to the role discovery task.

IoU Regression with H+L-Sampling for Accurate Detection Confidence.

It is a common paradigm in object detection frameworks that the samples in training and testing have consistent distributions for the two main tasks: Classification and bounding box regression. This paradigm is popular in sampling strategy for training an object detector due to its intuition and practicability. For the task of localization quality estimation, there exist two ways of sampling: The same sampling with the main tasks and the uniform sampling by manually augmenting the ground-truth.

Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism.

Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different network tasks. Temporal networks are an important type of network whose topological structure changes over time. Compared with methods on static networks, temporal network embedding (TNE) methods are facing three challenges: 1) it cannot describe the temporal dependence across network snapshots; 2) the node embedding in the latent space fails to indicate changes in the network topology; and 3) it cannot avoid a lot of redundant computation via parameter inheritance on a series of snapshots.

Bioflocculation effect of Glyptotendipes tokunagai on different Microcystis species: Interactions between secreted silk and extracellular polymeric substances.

Cyanobacterial blooms are a major problem in many lakes and can negatively impact public health and ecosystem services. The bioflocculation technique has proven to be a cost-effective, environmentally friendly technique with no secondary pollution to harvest multiple microalgae; however, few studies have focused on its effect on and potential for controlling cyanobacterial blooms in eutrophic lakes. In this study, the bioflocculation efficiencies of different Microcystis species under Glyptotendipes tokunagai (Diptera, Chironomidae) stress conditions and the interactions between secreted silk from Chironomid larvae and extracellular polymeric substances (EPS) from Microcystis were compared.

Feedback regulation of surface scum formation and persistence by self-shading of Microcystis colonies: Numerical simulations and laboratory experiments.

Light availability is an important driver of algal growth and for the formation of surface blooms. The formation of Microcystis surface scum decreases the transparency of the water column and influences the vertical distribution of light intensity. Only few studies analysed the interactions between the dynamics of surface blooms and the light distribution in the water column.

Co-regulatory role of Microcystis colony cell volume and compactness in buoyancy during the growth stage.

The buoyancy of Microcystis colonies determines the occurrence and dominance of bloom on the water surface. Besides the cell density regulation and the formation of larger size aggregates, increases in cell volume per colony (V) and the colony's compactness (i.e.

Blocking the PD-1/PD-L1 axis enhanced cisplatin chemotherapy in osteosarcoma in vitro and in vivo.

Background: The blocking of the programmed cell death protein (PD-1)/programmed death-ligand 1 (PD-L1) axis has been found to have an anticancer activity against various types of cancer by enhancing T cell immunity, while there are no studies linking the PD-1/PD-L1 axis to chemotherapy drugs in osteosarcoma (OS). The present study aimed to investigate the effects of blocking PD-1/PD-L1 axis on the cisplatin chemotherapy in OS in vitro and in vivo.

Methods: Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was applied to detect PD-L1 mRNA in OS tissues.

A neural network based computational model to predict the output power of different types of photovoltaic cells.

In this article, we introduced an artificial neural network (ANN) based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-), multi-crystalline (multi-), and amorphous (amor-) crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells.

Enhanced bandgap in annular photonic-crystal silicon-on-insulator asymmetric slabs.

Photonic band structures of annular photonic-crystal (APC) silicon-on-insulator (SOI) asymmetric slabs with finite thickness were investigated by the three-dimensional plane-wave expansion method. The results show that for a broad range of air-volume filling factors, APC slabs can exhibit a significantly larger bandgap than conventional circular-hole photonic-crystal (PC) slabs. Bandgap enhancements over conventional air hole PC SOI slabs as large as twofold are predicted for low air-volume filling factors below 15%.

Wideband slow light in chirped slot photonic-crystal coupled waveguides.

Wideband dispersion-free slow light in chirped-slot photonic-crystal coupled waveguides is proposed and theoretically investigated in detail. By systematically analyzing the dependence of band shape on various structure parameters, unique inflection points in the key photonic band with approximate zero group velocity can be obtained in an optimized slot photonic-crystal coupled waveguide. By simply chirping the widths of the photonic-crystal waveguides in the optimized structure, wideband (up to 20 nm) slow-light with optical confinement in the low dielectric slot is demonstrated numerically with relative temporal pulse-width spreading well below 8% as obtained from two-dimensional finite-difference time-domain simulations.